🤖 AI Summary
Existing pose flow–based anomaly detection methods rely on a single flow score, which struggles to capture the multimodality of normal behaviors and is sensitive to pose observation noise, further lacking an effective calibration mechanism under frozen detector settings. This work proposes a lightweight post-processing calibration approach that, for the first time, integrates nearest prototype deviation in latent space with keypoint confidence gating to enable reliability-aware recalibration of the original flow scores—without requiring model retraining. Evaluated across two backbone networks and four benchmark datasets, the method consistently improves frame-level AUROC by 0.34–4.49 percentage points, achieving an average gain of 2.03 percentage points.
📝 Abstract
Pose-flow video anomaly detectors are attractive for one-class surveillance because they provide likelihood-based rankings for tracked skeleton windows. However, a single likelihood score may hide multimodal normal behavior and be sensitive to pose-observation noise. We study a frozen-detector setting in which the pose-flow backbone, cached skeleton tracks, and evaluation pipeline are fixed. Reliability-Aware Prototype Calibration (RPC) is a post-hoc score calibration method for this setting. It adds a standardized nearest-prototype deviation in the frozen latent space to the standardized flow score, and uses keypoint confidence only to gate this added geometric evidence. Thus, RPC preserves the original density signal while correcting the ranking with empirical normal-mode structure under pose reliability. Across two frozen pose-flow backbones and four datasets, RPC improves frame-level AUROC in all eight backbone-dataset pairs, with gains ranging from 0.34 to 4.49 percentage points and averaging 2.03 points. Ablation and reliability analyses show that prototype deviation is the main corrective signal, while reliability gating is most useful when pose observations are less trustworthy. These results suggest that lightweight post-hoc calibration can strengthen cached pose-flow systems when retraining or reproducing the full pose pipeline is impractical.